(A) Field of the Invention
The present invention relates to a digital circuit for textural feature extraction or texture classification.
(B) Description of Prior Art
In recent years many different techniques have been proposed for texture classification.
The classical Gray Tone Spatial Dependence Matrices GTSDM as disclosed in an article by R. M. Haralick, K. Shannugan, I. Dinstein, entitled "TEXTURAL FEATURES FOR IMAGE CLASSIFICATION", IEEE Trans. Syst., Man. Cybern, vol. SMC 8, pp. 10-621, November 1983, is perhaps one of the most popular algorithms for texture feature extraction. However, because of its voluminous data requirements, the method becomes economically unfeasible for high speed applications.
In an article entitled "Texture Energy Measures" by Kenneth I. Laws, in Proc. Image Understanding Workshop, pp. 47-51, November 1979, Laws teaches the use of some filters of dimension 3.times.3 or 5.times.5 for matching edges, spots, lines, ripple, etc. Although good results have been obtained, this approach is heuristic. The filter set is either uncomplete or not mutually orthogonal.
In a recent article by Michael Unser, entitled "Sum and Difference Histograms for texture classification", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 1, pp. 118-125, January 1986, Unser introduces the sum and the difference transformations for a pair of picture elements along a given direction. These transformations reduce the computational time and memory storage requirements compared to the Gray Tone Spatial Dependence Matrices (GTSDM). However, this method is conceptually equivalent to the GTSDM one by using a pair of picture elements.